Overview

Dataset statistics

Number of variables29
Number of observations74111
Missing cells83752
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory174.0 MiB
Average record size in memory2.4 KiB

Variable types

Numeric10
Categorical5
Text6
Boolean4
DateTime3
URL1

Alerts

accommodates is highly overall correlated with bedrooms and 2 other fieldsHigh correlation
bedrooms is highly overall correlated with accommodates and 1 other fieldsHigh correlation
beds is highly overall correlated with accommodates and 1 other fieldsHigh correlation
city is highly overall correlated with latitude and 1 other fieldsHigh correlation
latitude is highly overall correlated with city and 1 other fieldsHigh correlation
log_price is highly overall correlated with accommodatesHigh correlation
longitude is highly overall correlated with city and 1 other fieldsHigh correlation
property_type is highly imbalanced (68.7%)Imbalance
bed_type is highly imbalanced (89.7%)Imbalance
host_has_profile_pic is highly imbalanced (97.0%)Imbalance
first_review has 15864 (21.4%) missing valuesMissing
host_response_rate has 18299 (24.7%) missing valuesMissing
last_review has 15827 (21.4%) missing valuesMissing
neighbourhood has 6872 (9.3%) missing valuesMissing
review_scores_rating has 16722 (22.6%) missing valuesMissing
thumbnail_url has 8216 (11.1%) missing valuesMissing
zipcode has 966 (1.3%) missing valuesMissing
id has unique valuesUnique
latitude has unique valuesUnique
longitude has unique valuesUnique
number_of_reviews has 15819 (21.3%) zerosZeros
bedrooms has 6715 (9.1%) zerosZeros

Reproduction

Analysis started2025-12-05 17:52:02.204807
Analysis finished2025-12-05 17:52:19.102251
Duration16.9 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct74111
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11266617
Minimum344
Maximum21230903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size579.1 KiB
2025-12-05T18:52:19.137501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum344
5-th percentile837770
Q16261964.5
median12254147
Q316402260
95-th percentile20017610
Maximum21230903
Range21230559
Interquartile range (IQR)10140296

Descriptive statistics

Standard deviation6081734.9
Coefficient of variation (CV)0.53980133
Kurtosis-1.1336229
Mean11266617
Median Absolute Deviation (MAD)4854945
Skewness-0.2606158
Sum8.3498026 × 1011
Variance3.6987499 × 1013
MonotonicityNot monotonic
2025-12-05T18:52:19.187839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69012571
 
< 0.1%
63049281
 
< 0.1%
79194001
 
< 0.1%
134187791
 
< 0.1%
38087091
 
< 0.1%
124229351
 
< 0.1%
118255291
 
< 0.1%
139712731
 
< 0.1%
1807921
 
< 0.1%
53852601
 
< 0.1%
Other values (74101)74101
> 99.9%
ValueCountFrequency (%)
3441
< 0.1%
9411
< 0.1%
24041
< 0.1%
25151
< 0.1%
27321
< 0.1%
28641
< 0.1%
31521
< 0.1%
33301
< 0.1%
33621
< 0.1%
36621
< 0.1%
ValueCountFrequency (%)
212309031
< 0.1%
212283561
< 0.1%
212279731
< 0.1%
212274611
< 0.1%
212271961
< 0.1%
212189731
< 0.1%
212187511
< 0.1%
212177741
< 0.1%
212154511
< 0.1%
212137901
< 0.1%

log_price
Real number (ℝ)

High correlation 

Distinct767
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7820691
Minimum0
Maximum7.6004023
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size579.1 KiB
2025-12-05T18:52:19.234745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.6888795
Q14.3174881
median4.7095302
Q35.2203558
95-th percentile6.0520892
Maximum7.6004023
Range7.6004023
Interquartile range (IQR)0.90286771

Descriptive statistics

Standard deviation0.71739378
Coefficient of variation (CV)0.15001744
Kurtosis0.66060828
Mean4.7820691
Median Absolute Deviation (MAD)0.46103496
Skewness0.51469541
Sum354403.92
Variance0.51465384
MonotonicityNot monotonic
2025-12-05T18:52:19.282844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0106352942729
 
3.7%
4.6051701862707
 
3.7%
4.3174881142049
 
2.8%
5.2983173671923
 
2.6%
3.9120230051920
 
2.6%
4.0943445621800
 
2.4%
4.3820266351725
 
2.3%
4.8283137371713
 
2.3%
4.174387271635
 
2.2%
4.2484952421605
 
2.2%
Other values (757)54305
73.3%
ValueCountFrequency (%)
01
 
< 0.1%
1.6094379121
 
< 0.1%
2.30258509330
< 0.1%
2.3978952731
 
< 0.1%
2.484906655
 
< 0.1%
2.5649493571
 
< 0.1%
2.639057331
 
< 0.1%
2.70805020158
0.1%
2.77258872210
 
< 0.1%
2.83321334410
 
< 0.1%
ValueCountFrequency (%)
7.6004023355
< 0.1%
7.5983993296
< 0.1%
7.5958899181
 
< 0.1%
7.5908521241
 
< 0.1%
7.5883236771
 
< 0.1%
7.5755846527
< 0.1%
7.5694117921
 
< 0.1%
7.5496091657
< 0.1%
7.5469741181
 
< 0.1%
7.5438028681
 
< 0.1%

property_type
Categorical

Imbalance 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
Apartment
49003 
House
16511 
Condominium
 
2658
Townhouse
 
1692
Loft
 
1244
Other values (30)
 
3003

Length

Max length18
Median length9
Mean length8.0775054
Min length3

Characters and Unicode

Total characters598632
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowApartment
2nd rowApartment
3rd rowApartment
4th rowHouse
5th rowApartment

Common Values

ValueCountFrequency (%)
Apartment49003
66.1%
House16511
 
22.3%
Condominium2658
 
3.6%
Townhouse1692
 
2.3%
Loft1244
 
1.7%
Other607
 
0.8%
Guesthouse498
 
0.7%
Bed & Breakfast462
 
0.6%
Bungalow366
 
0.5%
Villa179
 
0.2%
Other values (25)891
 
1.2%

Length

2025-12-05T18:52:19.331097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apartment49024
65.1%
house16515
 
21.9%
condominium2658
 
3.5%
townhouse1692
 
2.2%
loft1244
 
1.7%
other607
 
0.8%
guesthouse498
 
0.7%
bed462
 
0.6%
462
 
0.6%
breakfast462
 
0.6%
Other values (32)1641
 
2.2%

Most occurring characters

ValueCountFrequency (%)
t101449
16.9%
e70575
11.8%
n56574
9.5%
m54664
9.1%
a50949
8.5%
r50452
8.4%
p49123
8.2%
A49003
8.2%
o27768
 
4.6%
u22637
 
3.8%
Other values (32)65438
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)598632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t101449
16.9%
e70575
11.8%
n56574
9.5%
m54664
9.1%
a50949
8.5%
r50452
8.4%
p49123
8.2%
A49003
8.2%
o27768
 
4.6%
u22637
 
3.8%
Other values (32)65438
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)598632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t101449
16.9%
e70575
11.8%
n56574
9.5%
m54664
9.1%
a50949
8.5%
r50452
8.4%
p49123
8.2%
A49003
8.2%
o27768
 
4.6%
u22637
 
3.8%
Other values (32)65438
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)598632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t101449
16.9%
e70575
11.8%
n56574
9.5%
m54664
9.1%
a50949
8.5%
r50452
8.4%
p49123
8.2%
A49003
8.2%
o27768
 
4.6%
u22637
 
3.8%
Other values (32)65438
10.9%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
Entire home/apt
41310 
Private room
30638 
Shared room
 
2163

Length

Max length15
Median length15
Mean length13.643035
Min length11

Characters and Unicode

Total characters1011099
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt41310
55.7%
Private room30638
41.3%
Shared room2163
 
2.9%

Length

2025-12-05T18:52:19.371733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-05T18:52:19.400868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
entire41310
27.9%
home/apt41310
27.9%
room32801
22.1%
private30638
20.7%
shared2163
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e115421
11.4%
t113258
11.2%
o106912
10.6%
r106912
10.6%
74111
 
7.3%
a74111
 
7.3%
m74111
 
7.3%
i71948
 
7.1%
h43473
 
4.3%
E41310
 
4.1%
Other values (7)189532
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1011099
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e115421
11.4%
t113258
11.2%
o106912
10.6%
r106912
10.6%
74111
 
7.3%
a74111
 
7.3%
m74111
 
7.3%
i71948
 
7.1%
h43473
 
4.3%
E41310
 
4.1%
Other values (7)189532
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1011099
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e115421
11.4%
t113258
11.2%
o106912
10.6%
r106912
10.6%
74111
 
7.3%
a74111
 
7.3%
m74111
 
7.3%
i71948
 
7.1%
h43473
 
4.3%
E41310
 
4.1%
Other values (7)189532
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1011099
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e115421
11.4%
t113258
11.2%
o106912
10.6%
r106912
10.6%
74111
 
7.3%
a74111
 
7.3%
m74111
 
7.3%
i71948
 
7.1%
h43473
 
4.3%
E41310
 
4.1%
Other values (7)189532
18.7%
Distinct67122
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size24.1 MiB
2025-12-05T18:52:19.459264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1496
Median length777
Mean length267.89765
Min length2

Characters and Unicode

Total characters19854163
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63597 ?
Unique (%)85.8%

Sample

1st row{"Wireless Internet","Air conditioning",Kitchen,Heating,"Family/kid friendly",Essentials,"Hair dryer",Iron,"translation missing: en.hosting_amenity_50"}
2nd row{"Wireless Internet","Air conditioning",Kitchen,Heating,"Family/kid friendly",Washer,Dryer,"Smoke detector","Fire extinguisher",Essentials,Shampoo,Hangers,"Hair dryer",Iron,"translation missing: en.hosting_amenity_50"}
3rd row{TV,"Cable TV","Wireless Internet","Air conditioning",Kitchen,Breakfast,"Buzzer/wireless intercom",Heating,"Family/kid friendly","Smoke detector","Carbon monoxide detector","Fire extinguisher",Essentials,Shampoo,Hangers,"Hair dryer",Iron,"Laptop friendly workspace","translation missing: en.hosting_amenity_50"}
4th row{TV,"Cable TV",Internet,"Wireless Internet",Kitchen,"Indoor fireplace","Buzzer/wireless intercom",Heating,Washer,Dryer,"Smoke detector","Carbon monoxide detector","First aid kit","Fire extinguisher",Essentials}
5th row{TV,Internet,"Wireless Internet","Air conditioning",Kitchen,"Elevator in building",Heating,"Smoke detector","Carbon monoxide detector","Fire extinguisher",Essentials,Shampoo}
ValueCountFrequency (%)
internet","air54116
 
4.9%
on51429
 
4.7%
monoxide47190
 
4.3%
detector","carbon46664
 
4.3%
missing45718
 
4.2%
friendly44137
 
4.0%
dryer",iron,"laptop29159
 
2.7%
aid27532
 
2.5%
detector","first26900
 
2.5%
parking23757
 
2.2%
Other values (6673)699688
63.8%
2025-12-05T18:52:19.576397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e1912718
 
9.6%
"1404232
 
7.1%
n1391739
 
7.0%
i1373633
 
6.9%
r1365318
 
6.9%
,1230421
 
6.2%
t1212287
 
6.1%
o1106460
 
5.6%
1022179
 
5.1%
s913018
 
4.6%
Other values (53)6922158
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)19854163
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1912718
 
9.6%
"1404232
 
7.1%
n1391739
 
7.0%
i1373633
 
6.9%
r1365318
 
6.9%
,1230421
 
6.2%
t1212287
 
6.1%
o1106460
 
5.6%
1022179
 
5.1%
s913018
 
4.6%
Other values (53)6922158
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)19854163
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1912718
 
9.6%
"1404232
 
7.1%
n1391739
 
7.0%
i1373633
 
6.9%
r1365318
 
6.9%
,1230421
 
6.2%
t1212287
 
6.1%
o1106460
 
5.6%
1022179
 
5.1%
s913018
 
4.6%
Other values (53)6922158
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)19854163
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1912718
 
9.6%
"1404232
 
7.1%
n1391739
 
7.0%
i1373633
 
6.9%
r1365318
 
6.9%
,1230421
 
6.2%
t1212287
 
6.1%
o1106460
 
5.6%
1022179
 
5.1%
s913018
 
4.6%
Other values (53)6922158
34.9%

accommodates
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1551457
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size579.1 KiB
2025-12-05T18:52:19.611470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile7
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.153589
Coefficient of variation (CV)0.68256404
Kurtosis7.4324614
Mean3.1551457
Median Absolute Deviation (MAD)1
Skewness2.2315606
Sum233831
Variance4.6379454
MonotonicityNot monotonic
2025-12-05T18:52:19.644428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
231854
43.0%
412066
 
16.3%
19434
 
12.7%
37794
 
10.5%
64969
 
6.7%
53444
 
4.6%
81795
 
2.4%
7946
 
1.3%
10701
 
0.9%
16301
 
0.4%
Other values (6)807
 
1.1%
ValueCountFrequency (%)
19434
 
12.7%
231854
43.0%
37794
 
10.5%
412066
 
16.3%
53444
 
4.6%
64969
 
6.7%
7946
 
1.3%
81795
 
2.4%
9270
 
0.4%
10701
 
0.9%
ValueCountFrequency (%)
16301
 
0.4%
1554
 
0.1%
14104
 
0.1%
1336
 
< 0.1%
12264
 
0.4%
1179
 
0.1%
10701
 
0.9%
9270
 
0.4%
81795
2.4%
7946
1.3%

bathrooms
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing200
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.2352627
Minimum0
Maximum8
Zeros198
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size579.1 KiB
2025-12-05T18:52:19.679291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5820441
Coefficient of variation (CV)0.47119055
Kurtosis22.236542
Mean1.2352627
Median Absolute Deviation (MAD)0
Skewness3.6914529
Sum91299.5
Variance0.33877533
MonotonicityNot monotonic
2025-12-05T18:52:19.715890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
158099
78.4%
27936
 
10.7%
1.53801
 
5.1%
2.51567
 
2.1%
31066
 
1.4%
3.5429
 
0.6%
4286
 
0.4%
0.5209
 
0.3%
0198
 
0.3%
4.5116
 
0.2%
Other values (7)204
 
0.3%
(Missing)200
 
0.3%
ValueCountFrequency (%)
0198
 
0.3%
0.5209
 
0.3%
158099
78.4%
1.53801
 
5.1%
27936
 
10.7%
2.51567
 
2.1%
31066
 
1.4%
3.5429
 
0.6%
4286
 
0.4%
4.5116
 
0.2%
ValueCountFrequency (%)
841
 
0.1%
7.56
 
< 0.1%
710
 
< 0.1%
6.512
 
< 0.1%
624
 
< 0.1%
5.539
 
0.1%
572
 
0.1%
4.5116
 
0.2%
4286
0.4%
3.5429
0.6%

bed_type
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
Real Bed
72028 
Futon
 
753
Pull-out Sofa
 
585
Airbed
 
477
Couch
 
268

Length

Max length13
Median length8
Mean length7.9852653
Min length5

Characters and Unicode

Total characters591796
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReal Bed
2nd rowReal Bed
3rd rowReal Bed
4th rowReal Bed
5th rowReal Bed

Common Values

ValueCountFrequency (%)
Real Bed72028
97.2%
Futon753
 
1.0%
Pull-out Sofa585
 
0.8%
Airbed477
 
0.6%
Couch268
 
0.4%

Length

2025-12-05T18:52:19.759378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-05T18:52:19.791348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
real72028
49.1%
bed72028
49.1%
futon753
 
0.5%
pull-out585
 
0.4%
sofa585
 
0.4%
airbed477
 
0.3%
couch268
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e144533
24.4%
l73198
12.4%
a72613
12.3%
72613
12.3%
d72505
12.3%
R72028
12.2%
B72028
12.2%
u2191
 
0.4%
o2191
 
0.4%
t1338
 
0.2%
Other values (13)6558
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)591796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e144533
24.4%
l73198
12.4%
a72613
12.3%
72613
12.3%
d72505
12.3%
R72028
12.2%
B72028
12.2%
u2191
 
0.4%
o2191
 
0.4%
t1338
 
0.2%
Other values (13)6558
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)591796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e144533
24.4%
l73198
12.4%
a72613
12.3%
72613
12.3%
d72505
12.3%
R72028
12.2%
B72028
12.2%
u2191
 
0.4%
o2191
 
0.4%
t1338
 
0.2%
Other values (13)6558
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)591796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e144533
24.4%
l73198
12.4%
a72613
12.3%
72613
12.3%
d72505
12.3%
R72028
12.2%
B72028
12.2%
u2191
 
0.4%
o2191
 
0.4%
t1338
 
0.2%
Other values (13)6558
 
1.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
strict
32374 
flexible
22545 
moderate
19063 
super_strict_30
 
112
super_strict_60
 
17

Length

Max length15
Median length8
Mean length7.1385219
Min length6

Characters and Unicode

Total characters529043
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstrict
2nd rowstrict
3rd rowmoderate
4th rowflexible
5th rowmoderate

Common Values

ValueCountFrequency (%)
strict32374
43.7%
flexible22545
30.4%
moderate19063
25.7%
super_strict_30112
 
0.2%
super_strict_6017
 
< 0.1%

Length

2025-12-05T18:52:19.827900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-05T18:52:19.857740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
strict32374
43.7%
flexible22545
30.4%
moderate19063
25.7%
super_strict_30112
 
0.2%
super_strict_6017
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t84069
15.9%
e83345
15.8%
i55048
10.4%
r51695
9.8%
l45090
8.5%
s32632
 
6.2%
c32503
 
6.1%
f22545
 
4.3%
x22545
 
4.3%
b22545
 
4.3%
Other values (10)77026
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)529043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t84069
15.9%
e83345
15.8%
i55048
10.4%
r51695
9.8%
l45090
8.5%
s32632
 
6.2%
c32503
 
6.1%
f22545
 
4.3%
x22545
 
4.3%
b22545
 
4.3%
Other values (10)77026
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)529043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t84069
15.9%
e83345
15.8%
i55048
10.4%
r51695
9.8%
l45090
8.5%
s32632
 
6.2%
c32503
 
6.1%
f22545
 
4.3%
x22545
 
4.3%
b22545
 
4.3%
Other values (10)77026
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)529043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t84069
15.9%
e83345
15.8%
i55048
10.4%
r51695
9.8%
l45090
8.5%
s32632
 
6.2%
c32503
 
6.1%
f22545
 
4.3%
x22545
 
4.3%
b22545
 
4.3%
Other values (10)77026
14.6%

cleaning_fee
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.5 KiB
True
54403 
False
19708 
ValueCountFrequency (%)
True54403
73.4%
False19708
 
26.6%
2025-12-05T18:52:19.885698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

city
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
NYC
32349 
LA
22453 
SF
6434 
DC
5688 
Chicago
3719 

Length

Max length7
Median length6
Mean length2.87458
Min length2

Characters and Unicode

Total characters213038
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNYC
2nd rowNYC
3rd rowNYC
4th rowSF
5th rowDC

Common Values

ValueCountFrequency (%)
NYC32349
43.6%
LA22453
30.3%
SF6434
 
8.7%
DC5688
 
7.7%
Chicago3719
 
5.0%
Boston3468
 
4.7%

Length

2025-12-05T18:52:19.916609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-05T18:52:19.950320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
nyc32349
43.6%
la22453
30.3%
sf6434
 
8.7%
dc5688
 
7.7%
chicago3719
 
5.0%
boston3468
 
4.7%

Most occurring characters

ValueCountFrequency (%)
C41756
19.6%
N32349
15.2%
Y32349
15.2%
L22453
10.5%
A22453
10.5%
o10655
 
5.0%
F6434
 
3.0%
S6434
 
3.0%
D5688
 
2.7%
h3719
 
1.7%
Other values (8)28748
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)213038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C41756
19.6%
N32349
15.2%
Y32349
15.2%
L22453
10.5%
A22453
10.5%
o10655
 
5.0%
F6434
 
3.0%
S6434
 
3.0%
D5688
 
2.7%
h3719
 
1.7%
Other values (8)28748
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)213038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C41756
19.6%
N32349
15.2%
Y32349
15.2%
L22453
10.5%
A22453
10.5%
o10655
 
5.0%
F6434
 
3.0%
S6434
 
3.0%
D5688
 
2.7%
h3719
 
1.7%
Other values (8)28748
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)213038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C41756
19.6%
N32349
15.2%
Y32349
15.2%
L22453
10.5%
A22453
10.5%
o10655
 
5.0%
F6434
 
3.0%
S6434
 
3.0%
D5688
 
2.7%
h3719
 
1.7%
Other values (8)28748
13.5%
Distinct73479
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size75.5 MiB
2025-12-05T18:52:20.090026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1000
Median length1000
Mean length762.68171
Min length1

Characters and Unicode

Total characters56523104
Distinct characters2992
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72975 ?
Unique (%)98.5%

Sample

1st rowBeautiful, sunlit brownstone 1-bedroom in the loveliest neighborhood in Brooklyn. Blocks from the promenade and Brooklyn Bridge Park, with their stunning views of Manhattan, and from the great shopping and food.
2nd rowEnjoy travelling during your stay in Manhattan. My place is centrally located near Times Square and Central Park with easy access to main subways as well as walking distance to many popular restaurants and bus tours. My place is close to the subway, Totto Ramen, Hell's Kitchen, Ippudo Westside, Empanada Mama, Intrepid Sea, Air & Space Museum. My place has three true bedrooms and one bathroom. The kitchen is stocked with stainless steel appliances like the Keurig machine. The living room is spacious and can accommodate another person thanks to the pull out bed. My place is centrally located to some of the top attractions in the city. Feel free to explore the entire apartment and do not worry about sharing the space with any strangers. This is all yours during your stay. I am available via text/email/phone for anything you might need. - Times Square - Rockefeller Plaza - Central Park - 5th Avenue Shopping -Broadway Theater District - Empire State Building - Hudson River Express Subwa
3rd rowThe Oasis comes complete with a full backyard with outdoor furniture to make the most of this summer vacation!! The unit has high ceilings, a completed renovation throughout, beautiful flood lighting and exposed brick! Best part, total seclusion. You share with no one! The entire unit is yours during your stay. It's a fully furnished apartment that can hold up to 5 people. The only items you need are a toothbrush and your luggage!!! The unit has high ceilings, a completed renovation throughout, beautiful flood lighting and exposed brick! Not to mention the large backyard complete with ourdoor furniture. Best part, total seclusion. You share with no one! The entire unit is yours during your stay. The entire unit and backyard Garden area My assistant is available off site via phone. Other than that, you will be alone for your stay. The neighborhood of central Harlem is very diverse and full of culture! We are a few blocks from Historic 125th Street, The Apollo Theatre, The Schomburg
4th rowThis light-filled home-away-from-home is super clean and comes with all of the modern amenities travelers could want. Located on a quiet street in the very trendy, super central Lower Haight neighborhood. 15 minutes to Superbowl City! Avail 2/4-2/8. Tucked away on a quaint and quiet one-way street just one block away from the restaurants, shops and bars on Haight Street. There are trains and buses just a block away to get around the city. Super central and trendy!
5th rowCool, cozy, and comfortable studio located in the heart of the city. Literally a 10 min walk to Columbia Heights metro where you'll find shopping, theater, bars and diverse restaurants. Fits two very well, and is very convenient to other great areas. Welcome home! It's in a diverse building, where you meet alot of international people. It's located around different embassies, whom may provide a tour. Theres tons of people from over the world! Tons of different restaurants, and cultural things to do. Less than a mile walk to Dupont Circle, U Street, Adams Morgan, Columbia Heights metro. Tons of buses, and underground metro at your fingertips I can provide groceries for an affordable fee
ValueCountFrequency (%)
the431388
 
4.5%
and397823
 
4.1%
a277040
 
2.9%
to244175
 
2.5%
is190973
 
2.0%
in182645
 
1.9%
of158991
 
1.6%
with141925
 
1.5%
you101035
 
1.0%
97252
 
1.0%
Other values (97236)7442561
77.0%
2025-12-05T18:52:20.280927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9771175
17.3%
e4842280
 
8.6%
a3723357
 
6.6%
o3610804
 
6.4%
t3514255
 
6.2%
n2948896
 
5.2%
i2916768
 
5.2%
r2786943
 
4.9%
s2633670
 
4.7%
l2022797
 
3.6%
Other values (2982)17752159
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)56523104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9771175
17.3%
e4842280
 
8.6%
a3723357
 
6.6%
o3610804
 
6.4%
t3514255
 
6.2%
n2948896
 
5.2%
i2916768
 
5.2%
r2786943
 
4.9%
s2633670
 
4.7%
l2022797
 
3.6%
Other values (2982)17752159
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)56523104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9771175
17.3%
e4842280
 
8.6%
a3723357
 
6.6%
o3610804
 
6.4%
t3514255
 
6.2%
n2948896
 
5.2%
i2916768
 
5.2%
r2786943
 
4.9%
s2633670
 
4.7%
l2022797
 
3.6%
Other values (2982)17752159
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)56523104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9771175
17.3%
e4842280
 
8.6%
a3723357
 
6.6%
o3610804
 
6.4%
t3514255
 
6.2%
n2948896
 
5.2%
i2916768
 
5.2%
r2786943
 
4.9%
s2633670
 
4.7%
l2022797
 
3.6%
Other values (2982)17752159
31.4%

first_review
Date

Missing 

Distinct2554
Distinct (%)4.4%
Missing15864
Missing (%)21.4%
Memory size579.1 KiB
Minimum2008-11-17 00:00:00
Maximum2017-10-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-05T18:52:20.330508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:20.377845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

host_has_profile_pic
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing188
Missing (%)0.3%
Memory size144.9 KiB
True
73697 
False
 
226
(Missing)
 
188
ValueCountFrequency (%)
True73697
99.4%
False226
 
0.3%
(Missing)188
 
0.3%
2025-12-05T18:52:20.418147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing188
Missing (%)0.3%
Memory size144.9 KiB
True
49748 
False
24175 
(Missing)
 
188
ValueCountFrequency (%)
True49748
67.1%
False24175
32.6%
(Missing)188
 
0.3%
2025-12-05T18:52:20.435273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

host_response_rate
Text

Missing 

Distinct80
Distinct (%)0.1%
Missing18299
Missing (%)24.7%
Memory size3.4 MiB
2025-12-05T18:52:20.479583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7591557
Min length2

Characters and Unicode

Total characters209806
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row100%
2nd row100%
3rd row100%
4th row100%
5th row100%
ValueCountFrequency (%)
10043254
77.5%
902277
 
4.1%
801113
 
2.0%
0883
 
1.6%
50611
 
1.1%
70508
 
0.9%
99448
 
0.8%
67433
 
0.8%
98425
 
0.8%
94401
 
0.7%
Other values (70)5459
 
9.8%
2025-12-05T18:52:20.570504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
092456
44.1%
%55812
26.6%
143724
20.8%
96307
 
3.0%
83605
 
1.7%
72254
 
1.1%
61649
 
0.8%
51599
 
0.8%
31081
 
0.5%
4734
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)209806
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
092456
44.1%
%55812
26.6%
143724
20.8%
96307
 
3.0%
83605
 
1.7%
72254
 
1.1%
61649
 
0.8%
51599
 
0.8%
31081
 
0.5%
4734
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)209806
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
092456
44.1%
%55812
26.6%
143724
20.8%
96307
 
3.0%
83605
 
1.7%
72254
 
1.1%
61649
 
0.8%
51599
 
0.8%
31081
 
0.5%
4734
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)209806
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
092456
44.1%
%55812
26.6%
143724
20.8%
96307
 
3.0%
83605
 
1.7%
72254
 
1.1%
61649
 
0.8%
51599
 
0.8%
31081
 
0.5%
4734
 
0.3%
Distinct3087
Distinct (%)4.2%
Missing188
Missing (%)0.3%
Memory size579.1 KiB
Minimum2008-03-03 00:00:00
Maximum2017-10-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-05T18:52:20.612808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:20.662465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.5 KiB
False
54660 
True
19451 
ValueCountFrequency (%)
False54660
73.8%
True19451
 
26.2%
2025-12-05T18:52:20.697980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

last_review
Date

Missing 

Distinct1371
Distinct (%)2.4%
Missing15827
Missing (%)21.4%
Memory size579.1 KiB
Minimum2009-01-21 00:00:00
Maximum2017-10-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-05T18:52:20.730589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:20.777098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

latitude
Real number (ℝ)

High correlation  Unique 

Distinct74111
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.445958
Minimum33.338905
Maximum42.390437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size579.1 KiB
2025-12-05T18:52:20.824351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum33.338905
5-th percentile33.989601
Q134.127908
median40.662138
Q340.746096
95-th percentile41.985322
Maximum42.390437
Range9.0515325
Interquartile range (IQR)6.6181882

Descriptive statistics

Standard deviation3.0801666
Coefficient of variation (CV)0.080116785
Kurtosis-1.413862
Mean38.445958
Median Absolute Deviation (MAD)1.6688667
Skewness-0.53476625
Sum2849268.4
Variance9.4874261
MonotonicityNot monotonic
2025-12-05T18:52:20.871279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.696523631
 
< 0.1%
40.766115421
 
< 0.1%
40.808109991
 
< 0.1%
37.772004481
 
< 0.1%
38.925626921
 
< 0.1%
37.753164051
 
< 0.1%
33.98045441
 
< 0.1%
34.046737411
 
< 0.1%
37.781127981
 
< 0.1%
33.992562981
 
< 0.1%
Other values (74101)74101
> 99.9%
ValueCountFrequency (%)
33.338904671
< 0.1%
33.339001881
< 0.1%
33.339006661
< 0.1%
33.33932741
< 0.1%
33.340520971
< 0.1%
33.340916321
< 0.1%
33.343010971
< 0.1%
33.343287061
< 0.1%
33.343573681
< 0.1%
33.343624331
< 0.1%
ValueCountFrequency (%)
42.390437181
< 0.1%
42.390247541
< 0.1%
42.389906691
< 0.1%
42.389828271
< 0.1%
42.389788141
< 0.1%
42.389772451
< 0.1%
42.389681731
< 0.1%
42.389652771
< 0.1%
42.389530631
< 0.1%
42.389031481
< 0.1%

longitude
Real number (ℝ)

High correlation  Unique 

Distinct74111
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-92.397525
Minimum-122.5115
Maximum-70.985047
Zeros0
Zeros (%)0.0%
Negative74111
Negative (%)100.0%
Memory size579.1 KiB
2025-12-05T18:52:21.209214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-122.5115
5-th percentile-122.42291
Q1-118.34237
median-76.996965
Q3-73.95466
95-th percentile-73.776479
Maximum-70.985047
Range51.526453
Interquartile range (IQR)44.387714

Descriptive statistics

Standard deviation21.705322
Coefficient of variation (CV)-0.23491237
Kurtosis-1.7734749
Mean-92.397525
Median Absolute Deviation (MAD)3.1454079
Skewness-0.40709996
Sum-6847673
Variance471.121
MonotonicityNot monotonic
2025-12-05T18:52:21.255397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.991616851
 
< 0.1%
-73.989039921
 
< 0.1%
-73.943755841
 
< 0.1%
-122.43161871
 
< 0.1%
-77.034595521
 
< 0.1%
-122.42952611
 
< 0.1%
-118.46282071
 
< 0.1%
-118.26043891
 
< 0.1%
-122.50109521
 
< 0.1%
-117.89599661
 
< 0.1%
Other values (74101)74101
> 99.9%
ValueCountFrequency (%)
-122.51151
< 0.1%
-122.51094051
< 0.1%
-122.51090531
< 0.1%
-122.50963521
< 0.1%
-122.50936741
< 0.1%
-122.50936481
< 0.1%
-122.50933561
< 0.1%
-122.50925051
< 0.1%
-122.50923191
< 0.1%
-122.509161
< 0.1%
ValueCountFrequency (%)
-70.98504661
< 0.1%
-70.989358531
< 0.1%
-70.991861481
< 0.1%
-70.999165631
< 0.1%
-71.000260541
< 0.1%
-71.000461591
< 0.1%
-71.001548031
< 0.1%
-71.001769441
< 0.1%
-71.00212281
< 0.1%
-71.004619761
< 0.1%

name
Text

Distinct73359
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2025-12-05T18:52:21.365477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length179
Median length73
Mean length34.847715
Min length1

Characters and Unicode

Total characters2582599
Distinct characters1202
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72750 ?
Unique (%)98.2%

Sample

1st rowBeautiful brownstone 1-bedroom
2nd rowSuperb 3BR Apt Located Near Times Square
3rd rowThe Garden Oasis
4th rowBeautiful Flat in the Heart of SF!
5th rowGreat studio in midtown DC
ValueCountFrequency (%)
in22336
 
5.2%
room12539
 
2.9%
11187
 
2.6%
private10305
 
2.4%
bedroom9114
 
2.1%
apartment7375
 
1.7%
cozy6242
 
1.5%
the6241
 
1.5%
apt5687
 
1.3%
studio5225
 
1.2%
Other values (18558)330418
77.4%
2025-12-05T18:52:21.534252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
355038
 
13.7%
e187195
 
7.2%
o182717
 
7.1%
a147686
 
5.7%
t145892
 
5.6%
i135968
 
5.3%
n131309
 
5.1%
r130915
 
5.1%
l77952
 
3.0%
s69094
 
2.7%
Other values (1192)1018833
39.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)2582599
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
355038
 
13.7%
e187195
 
7.2%
o182717
 
7.1%
a147686
 
5.7%
t145892
 
5.6%
i135968
 
5.3%
n131309
 
5.1%
r130915
 
5.1%
l77952
 
3.0%
s69094
 
2.7%
Other values (1192)1018833
39.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2582599
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
355038
 
13.7%
e187195
 
7.2%
o182717
 
7.1%
a147686
 
5.7%
t145892
 
5.6%
i135968
 
5.3%
n131309
 
5.1%
r130915
 
5.1%
l77952
 
3.0%
s69094
 
2.7%
Other values (1192)1018833
39.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2582599
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
355038
 
13.7%
e187195
 
7.2%
o182717
 
7.1%
a147686
 
5.7%
t145892
 
5.6%
i135968
 
5.3%
n131309
 
5.1%
r130915
 
5.1%
l77952
 
3.0%
s69094
 
2.7%
Other values (1192)1018833
39.4%

neighbourhood
Text

Missing 

Distinct619
Distinct (%)0.9%
Missing6872
Missing (%)9.3%
Memory size4.1 MiB
2025-12-05T18:52:21.641747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length28
Mean length11.865971
Min length4

Characters and Unicode

Total characters797856
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)0.1%

Sample

1st rowBrooklyn Heights
2nd rowHell's Kitchen
3rd rowHarlem
4th rowLower Haight
5th rowColumbia Heights
ValueCountFrequency (%)
east4862
 
4.3%
heights4530
 
4.0%
west3702
 
3.3%
side3484
 
3.1%
park3230
 
2.8%
hollywood3002
 
2.6%
williamsburg2862
 
2.5%
upper2602
 
2.3%
hill2555
 
2.3%
harlem2167
 
1.9%
Other values (607)80461
70.9%
2025-12-05T18:52:21.790353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e68774
 
8.6%
i58130
 
7.3%
a53368
 
6.7%
t50083
 
6.3%
o49977
 
6.3%
l49298
 
6.2%
46224
 
5.8%
s44487
 
5.6%
r44041
 
5.5%
n39745
 
5.0%
Other values (48)293729
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)797856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e68774
 
8.6%
i58130
 
7.3%
a53368
 
6.7%
t50083
 
6.3%
o49977
 
6.3%
l49298
 
6.2%
46224
 
5.8%
s44487
 
5.6%
r44041
 
5.5%
n39745
 
5.0%
Other values (48)293729
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)797856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e68774
 
8.6%
i58130
 
7.3%
a53368
 
6.7%
t50083
 
6.3%
o49977
 
6.3%
l49298
 
6.2%
46224
 
5.8%
s44487
 
5.6%
r44041
 
5.5%
n39745
 
5.0%
Other values (48)293729
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)797856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e68774
 
8.6%
i58130
 
7.3%
a53368
 
6.7%
t50083
 
6.3%
o49977
 
6.3%
l49298
 
6.2%
46224
 
5.8%
s44487
 
5.6%
r44041
 
5.5%
n39745
 
5.0%
Other values (48)293729
36.8%

number_of_reviews
Real number (ℝ)

Zeros 

Distinct371
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.900568
Minimum0
Maximum605
Zeros15819
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size579.1 KiB
2025-12-05T18:52:21.834128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q323
95-th percentile95
Maximum605
Range605
Interquartile range (IQR)22

Descriptive statistics

Standard deviation37.828641
Coefficient of variation (CV)1.8099336
Kurtosis20.650051
Mean20.900568
Median Absolute Deviation (MAD)6
Skewness3.702835
Sum1548962
Variance1431.0061
MonotonicityNot monotonic
2025-12-05T18:52:21.877222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015819
21.3%
17106
 
9.6%
24750
 
6.4%
33661
 
4.9%
42912
 
3.9%
52518
 
3.4%
62032
 
2.7%
71851
 
2.5%
81678
 
2.3%
91441
 
1.9%
Other values (361)30343
40.9%
ValueCountFrequency (%)
015819
21.3%
17106
9.6%
24750
 
6.4%
33661
 
4.9%
42912
 
3.9%
52518
 
3.4%
62032
 
2.7%
71851
 
2.5%
81678
 
2.3%
91441
 
1.9%
ValueCountFrequency (%)
6051
< 0.1%
5421
< 0.1%
5321
< 0.1%
5301
< 0.1%
5251
< 0.1%
5051
< 0.1%
4951
< 0.1%
4921
< 0.1%
4801
< 0.1%
4741
< 0.1%

review_scores_rating
Real number (ℝ)

Missing 

Distinct54
Distinct (%)0.1%
Missing16722
Missing (%)22.6%
Infinite0
Infinite (%)0.0%
Mean94.067365
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size579.1 KiB
2025-12-05T18:52:21.924630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile80
Q192
median96
Q3100
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.8365561
Coefficient of variation (CV)0.083307916
Kurtosis20.021052
Mean94.067365
Median Absolute Deviation (MAD)4
Skewness-3.3808606
Sum5398432
Variance61.411612
MonotonicityNot monotonic
2025-12-05T18:52:21.973819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10016215
21.9%
984374
 
5.9%
974087
 
5.5%
964081
 
5.5%
953713
 
5.0%
933647
 
4.9%
902852
 
3.8%
992631
 
3.6%
942618
 
3.5%
802163
 
2.9%
Other values (44)11008
14.9%
(Missing)16722
22.6%
ValueCountFrequency (%)
2097
0.1%
272
 
< 0.1%
304
 
< 0.1%
351
 
< 0.1%
4090
0.1%
475
 
< 0.1%
491
 
< 0.1%
5030
 
< 0.1%
5310
 
< 0.1%
541
 
< 0.1%
ValueCountFrequency (%)
10016215
21.9%
992631
 
3.6%
984374
 
5.9%
974087
 
5.5%
964081
 
5.5%
953713
 
5.0%
942618
 
3.5%
933647
 
4.9%
922064
 
2.8%
911615
 
2.2%

thumbnail_url
URL

Missing 

Distinct65883
Distinct (%)> 99.9%
Missing8216
Missing (%)11.1%
Memory size9.0 MiB
https://a0.muscache.com/im/pictures/70087089/bc66229a_original.jpg?aki_policy=small
 
3
https://a0.muscache.com/im/pictures/4491e5c5-33f6-4704-9887-76a059f86fda.jpg?aki_policy=small
 
2
https://a0.muscache.com/im/pictures/28563531/1000de61_original.jpg?aki_policy=small
 
2
https://a0.muscache.com/im/pictures/105275678/2ec252ae_original.jpg?aki_policy=small
 
2
https://a0.muscache.com/im/pictures/23033013/54d62516_original.jpg?aki_policy=small
 
2
Other values (65878)
65884 
(Missing)
8216 
ValueCountFrequency (%)
https://a0.muscache.com/im/pictures/70087089/bc66229a_original.jpg?aki_policy=small3
 
< 0.1%
https://a0.muscache.com/im/pictures/4491e5c5-33f6-4704-9887-76a059f86fda.jpg?aki_policy=small2
 
< 0.1%
https://a0.muscache.com/im/pictures/28563531/1000de61_original.jpg?aki_policy=small2
 
< 0.1%
https://a0.muscache.com/im/pictures/105275678/2ec252ae_original.jpg?aki_policy=small2
 
< 0.1%
https://a0.muscache.com/im/pictures/23033013/54d62516_original.jpg?aki_policy=small2
 
< 0.1%
https://a0.muscache.com/im/pictures/109405834/9a555e66_original.jpg?aki_policy=small2
 
< 0.1%
https://a0.muscache.com/im/pictures/61042471/5543b0e0_original.jpg?aki_policy=small2
 
< 0.1%
https://a0.muscache.com/im/pictures/95059281/d8bfc436_original.jpg?aki_policy=small2
 
< 0.1%
https://a0.muscache.com/im/pictures/104667326/a7a2b145_original.jpg?aki_policy=small2
 
< 0.1%
https://a0.muscache.com/im/pictures/623a5884-0613-4cbd-962f-bbd28c7f47bc.jpg?aki_policy=small2
 
< 0.1%
Other values (65873)65874
88.9%
(Missing)8216
 
11.1%
ValueCountFrequency (%)
https65895
88.9%
(Missing)8216
 
11.1%
ValueCountFrequency (%)
a0.muscache.com65895
88.9%
(Missing)8216
 
11.1%
ValueCountFrequency (%)
/im/pictures/70087089/bc66229a_original.jpg3
 
< 0.1%
/im/pictures/4491e5c5-33f6-4704-9887-76a059f86fda.jpg2
 
< 0.1%
/im/pictures/28563531/1000de61_original.jpg2
 
< 0.1%
/im/pictures/105275678/2ec252ae_original.jpg2
 
< 0.1%
/im/pictures/23033013/54d62516_original.jpg2
 
< 0.1%
/im/pictures/109405834/9a555e66_original.jpg2
 
< 0.1%
/im/pictures/61042471/5543b0e0_original.jpg2
 
< 0.1%
/im/pictures/95059281/d8bfc436_original.jpg2
 
< 0.1%
/im/pictures/104667326/a7a2b145_original.jpg2
 
< 0.1%
/im/pictures/623a5884-0613-4cbd-962f-bbd28c7f47bc.jpg2
 
< 0.1%
Other values (65873)65874
88.9%
(Missing)8216
 
11.1%
ValueCountFrequency (%)
aki_policy=small65895
88.9%
(Missing)8216
 
11.1%
ValueCountFrequency (%)
65895
88.9%
(Missing)8216
 
11.1%

zipcode
Text

Missing 

Distinct769
Distinct (%)1.1%
Missing966
Missing (%)1.3%
Memory size3.8 MiB
2025-12-05T18:52:22.083021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length31
Median length5
Mean length5.2435847
Min length1

Characters and Unicode

Total characters383542
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)0.1%

Sample

1st row11201
2nd row10019
3rd row10027
4th row94117.0
5th row20009
ValueCountFrequency (%)
11211.01368
 
1.9%
902911274
 
1.7%
112211188
 
1.6%
94110988
 
1.4%
90046967
 
1.3%
20002952
 
1.3%
20009923
 
1.3%
20001820
 
1.1%
90028782
 
1.1%
10019775
 
1.1%
Other values (757)63109
86.3%
2025-12-05T18:52:22.234148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0109061
28.4%
193334
24.3%
247937
12.5%
937281
 
9.7%
621282
 
5.5%
319109
 
5.0%
418798
 
4.9%
510106
 
2.6%
79678
 
2.5%
.8857
 
2.3%
Other values (10)8099
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)383542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0109061
28.4%
193334
24.3%
247937
12.5%
937281
 
9.7%
621282
 
5.5%
319109
 
5.0%
418798
 
4.9%
510106
 
2.6%
79678
 
2.5%
.8857
 
2.3%
Other values (10)8099
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)383542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0109061
28.4%
193334
24.3%
247937
12.5%
937281
 
9.7%
621282
 
5.5%
319109
 
5.0%
418798
 
4.9%
510106
 
2.6%
79678
 
2.5%
.8857
 
2.3%
Other values (10)8099
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)383542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0109061
28.4%
193334
24.3%
247937
12.5%
937281
 
9.7%
621282
 
5.5%
319109
 
5.0%
418798
 
4.9%
510106
 
2.6%
79678
 
2.5%
.8857
 
2.3%
Other values (10)8099
 
2.1%

bedrooms
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing91
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.265793
Minimum0
Maximum10
Zeros6715
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size579.1 KiB
2025-12-05T18:52:22.274259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.85214349
Coefficient of variation (CV)0.67320918
Kurtosis7.5841023
Mean1.265793
Median Absolute Deviation (MAD)0
Skewness1.9898487
Sum93694
Variance0.72614853
MonotonicityNot monotonic
2025-12-05T18:52:22.307081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
149784
67.2%
211351
 
15.3%
06715
 
9.1%
34309
 
5.8%
41330
 
1.8%
5355
 
0.5%
6106
 
0.1%
738
 
0.1%
814
 
< 0.1%
1010
 
< 0.1%
(Missing)91
 
0.1%
ValueCountFrequency (%)
06715
 
9.1%
149784
67.2%
211351
 
15.3%
34309
 
5.8%
41330
 
1.8%
5355
 
0.5%
6106
 
0.1%
738
 
0.1%
814
 
< 0.1%
98
 
< 0.1%
ValueCountFrequency (%)
1010
 
< 0.1%
98
 
< 0.1%
814
 
< 0.1%
738
 
0.1%
6106
 
0.1%
5355
 
0.5%
41330
 
1.8%
34309
 
5.8%
211351
 
15.3%
149784
67.2%

beds
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing131
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.7108678
Minimum0
Maximum18
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size579.1 KiB
2025-12-05T18:52:22.338374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2541417
Coefficient of variation (CV)0.73304422
Kurtosis19.722605
Mean1.7108678
Median Absolute Deviation (MAD)0
Skewness3.3580002
Sum126570
Variance1.5728715
MonotonicityNot monotonic
2025-12-05T18:52:22.370464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
145144
60.9%
216704
 
22.5%
36442
 
8.7%
43065
 
4.1%
51287
 
1.7%
6661
 
0.9%
7216
 
0.3%
8186
 
0.3%
1088
 
0.1%
972
 
0.1%
Other values (8)115
 
0.2%
(Missing)131
 
0.2%
ValueCountFrequency (%)
04
 
< 0.1%
145144
60.9%
216704
 
22.5%
36442
 
8.7%
43065
 
4.1%
51287
 
1.7%
6661
 
0.9%
7216
 
0.3%
8186
 
0.3%
972
 
0.1%
ValueCountFrequency (%)
181
 
< 0.1%
1638
 
0.1%
156
 
< 0.1%
144
 
< 0.1%
1310
 
< 0.1%
1229
 
< 0.1%
1123
 
< 0.1%
1088
0.1%
972
 
0.1%
8186
0.3%

Interactions

2025-12-05T18:52:17.811275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:13.568217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:14.063730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:14.488208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:15.061262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:15.495731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:15.891257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.294851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.723841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T18:52:16.336375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.765377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:17.197371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:17.903385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:13.662399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:14.145443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T18:52:17.958061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T18:52:14.188120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T18:52:15.188017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:15.615045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.009585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T18:52:16.054406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.469583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.899336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:17.537559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:18.056304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T18:52:16.509253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.940308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T18:52:14.313816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:14.892476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:15.313803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:15.730145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.130728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.550037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.981303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:17.632124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:18.143950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T18:52:15.769199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.171397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.594610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T18:52:15.813741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T18:52:14.020313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T18:52:15.018099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:15.453628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:15.850604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.253013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:16.680929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:17.113004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T18:52:17.764190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-05T18:52:22.407987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
accommodatesbathroomsbed_typebedroomsbedscancellation_policycitycleaning_feehost_has_profile_pichost_identity_verifiedidinstant_bookablelatitudelog_pricelongitudenumber_of_reviewsproperty_typereview_scores_ratingroom_type
accommodates1.0000.3690.0470.5810.7830.1100.0710.1950.0210.055-0.0010.060-0.0600.589-0.1040.1260.083-0.0490.402
bathrooms0.3691.0000.0270.4930.4160.0640.0850.0740.0000.027-0.0010.024-0.1160.283-0.128-0.0340.1380.0240.139
bed_type0.0470.0271.0000.0370.0440.0220.0150.0320.0060.0180.0000.0290.0160.0570.0150.0040.0360.0000.177
bedrooms0.5810.4930.0371.0000.6410.0830.0610.1400.0060.028-0.0030.017-0.0470.408-0.068-0.0060.0950.0100.311
beds0.7830.4160.0440.6411.0000.0970.0560.1650.0050.042-0.0030.053-0.0630.481-0.0870.0850.099-0.0510.331
cancellation_policy0.1100.0640.0220.0830.0971.0000.0540.3580.0320.1800.0070.0230.0520.0890.0510.0760.0540.0580.131
city0.0710.0850.0150.0610.0560.0541.0000.0850.0000.0900.0020.0540.8940.0981.0000.0360.2040.0400.091
cleaning_fee0.1950.0740.0320.1400.1650.3580.0851.0000.0220.1620.0060.0090.0910.1560.0780.0760.0860.0620.214
host_has_profile_pic0.0210.0000.0060.0060.0050.0320.0000.0221.0000.0750.0060.0080.0100.0130.0000.0080.0000.0130.000
host_identity_verified0.0550.0270.0180.0280.0420.1800.0900.1620.0751.0000.0000.0880.0690.0630.0880.1220.0490.0730.070
id-0.001-0.0010.000-0.003-0.0030.0070.0020.0060.0060.0001.0000.000-0.003-0.008-0.002-0.0030.000-0.0020.000
instant_bookable0.0600.0240.0290.0170.0530.0230.0540.0090.0080.0880.0001.0000.0510.0540.0280.0700.0550.0810.029
latitude-0.060-0.1160.016-0.047-0.0630.0520.8940.0910.0100.069-0.0030.0511.0000.0050.7450.0040.188-0.0450.094
log_price0.5890.2830.0570.4080.4810.0890.0980.1560.0130.063-0.0080.0540.0051.000-0.162-0.0330.1050.0850.497
longitude-0.104-0.1280.015-0.068-0.0870.0511.0000.0780.0000.088-0.0020.0280.745-0.1621.000-0.0310.239-0.0710.079
number_of_reviews0.126-0.0340.004-0.0060.0850.0760.0360.0760.0080.122-0.0030.0700.004-0.033-0.0311.0000.059-0.2610.026
property_type0.0830.1380.0360.0950.0990.0540.2040.0860.0000.0490.0000.0550.1880.1050.2390.0591.0000.0400.166
review_scores_rating-0.0490.0240.0000.010-0.0510.0580.0400.0620.0130.073-0.0020.081-0.0450.085-0.071-0.2610.0401.0000.044
room_type0.4020.1390.1770.3110.3310.1310.0910.2140.0000.0700.0000.0290.0940.4970.0790.0260.1660.0441.000

Missing values

2025-12-05T18:52:18.345807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-05T18:52:18.545331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-05T18:52:18.825857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idlog_priceproperty_typeroom_typeamenitiesaccommodatesbathroomsbed_typecancellation_policycleaning_feecitydescriptionfirst_reviewhost_has_profile_pichost_identity_verifiedhost_response_ratehost_sinceinstant_bookablelast_reviewlatitudelongitudenameneighbourhoodnumber_of_reviewsreview_scores_ratingthumbnail_urlzipcodebedroomsbeds
069012575.010635ApartmentEntire home/apt{"Wireless Internet","Air conditioning",Kitchen,Heating,"Family/kid friendly",Essentials,"Hair dryer",Iron,"translation missing: en.hosting_amenity_50"}31.0Real BedstrictTrueNYCBeautiful, sunlit brownstone 1-bedroom in the loveliest neighborhood in Brooklyn. Blocks from the promenade and Brooklyn Bridge Park, with their stunning views of Manhattan, and from the great shopping and food.2016-06-18ttNaN2012-03-26f2016-07-1840.696524-73.991617Beautiful brownstone 1-bedroomBrooklyn Heights2100.0https://a0.muscache.com/im/pictures/6d7cbbf7-c034-459c-bc82-6522c957627c.jpg?aki_policy=small112011.01.0
163049285.129899ApartmentEntire home/apt{"Wireless Internet","Air conditioning",Kitchen,Heating,"Family/kid friendly",Washer,Dryer,"Smoke detector","Fire extinguisher",Essentials,Shampoo,Hangers,"Hair dryer",Iron,"translation missing: en.hosting_amenity_50"}71.0Real BedstrictTrueNYCEnjoy travelling during your stay in Manhattan. My place is centrally located near Times Square and Central Park with easy access to main subways as well as walking distance to many popular restaurants and bus tours. My place is close to the subway, Totto Ramen, Hell's Kitchen, Ippudo Westside, Empanada Mama, Intrepid Sea, Air & Space Museum. My place has three true bedrooms and one bathroom. The kitchen is stocked with stainless steel appliances like the Keurig machine. The living room is spacious and can accommodate another person thanks to the pull out bed. My place is centrally located to some of the top attractions in the city. Feel free to explore the entire apartment and do not worry about sharing the space with any strangers. This is all yours during your stay. I am available via text/email/phone for anything you might need. - Times Square - Rockefeller Plaza - Central Park - 5th Avenue Shopping -Broadway Theater District - Empire State Building - Hudson River Express Subwa2017-08-05tf100%2017-06-19t2017-09-2340.766115-73.989040Superb 3BR Apt Located Near Times SquareHell's Kitchen693.0https://a0.muscache.com/im/pictures/348a55fe-4b65-452a-b48a-bfecb3b58a66.jpg?aki_policy=small100193.03.0
279194004.976734ApartmentEntire home/apt{TV,"Cable TV","Wireless Internet","Air conditioning",Kitchen,Breakfast,"Buzzer/wireless intercom",Heating,"Family/kid friendly","Smoke detector","Carbon monoxide detector","Fire extinguisher",Essentials,Shampoo,Hangers,"Hair dryer",Iron,"Laptop friendly workspace","translation missing: en.hosting_amenity_50"}51.0Real BedmoderateTrueNYCThe Oasis comes complete with a full backyard with outdoor furniture to make the most of this summer vacation!! The unit has high ceilings, a completed renovation throughout, beautiful flood lighting and exposed brick! Best part, total seclusion. You share with no one! The entire unit is yours during your stay. It's a fully furnished apartment that can hold up to 5 people. The only items you need are a toothbrush and your luggage!!! The unit has high ceilings, a completed renovation throughout, beautiful flood lighting and exposed brick! Not to mention the large backyard complete with ourdoor furniture. Best part, total seclusion. You share with no one! The entire unit is yours during your stay. The entire unit and backyard Garden area My assistant is available off site via phone. Other than that, you will be alone for your stay. The neighborhood of central Harlem is very diverse and full of culture! We are a few blocks from Historic 125th Street, The Apollo Theatre, The Schomburg2017-04-30tt100%2016-10-25t2017-09-1440.808110-73.943756The Garden OasisHarlem1092.0https://a0.muscache.com/im/pictures/6fae5362-9e3a-4fa9-aa54-bbd5ea26538d.jpg?aki_policy=small100271.03.0
3134187796.620073HouseEntire home/apt{TV,"Cable TV",Internet,"Wireless Internet",Kitchen,"Indoor fireplace","Buzzer/wireless intercom",Heating,Washer,Dryer,"Smoke detector","Carbon monoxide detector","First aid kit","Fire extinguisher",Essentials}41.0Real BedflexibleTrueSFThis light-filled home-away-from-home is super clean and comes with all of the modern amenities travelers could want. Located on a quiet street in the very trendy, super central Lower Haight neighborhood. 15 minutes to Superbowl City! Avail 2/4-2/8. Tucked away on a quaint and quiet one-way street just one block away from the restaurants, shops and bars on Haight Street. There are trains and buses just a block away to get around the city. Super central and trendy!NaNttNaN2015-04-19fNaN37.772004-122.431619Beautiful Flat in the Heart of SF!Lower Haight0NaNhttps://a0.muscache.com/im/pictures/72208dad-9c86-41ea-a735-43d933111063.jpg?aki_policy=small94117.02.02.0
438087094.744932ApartmentEntire home/apt{TV,Internet,"Wireless Internet","Air conditioning",Kitchen,"Elevator in building",Heating,"Smoke detector","Carbon monoxide detector","Fire extinguisher",Essentials,Shampoo}21.0Real BedmoderateTrueDCCool, cozy, and comfortable studio located in the heart of the city. Literally a 10 min walk to Columbia Heights metro where you'll find shopping, theater, bars and diverse restaurants. Fits two very well, and is very convenient to other great areas. Welcome home! It's in a diverse building, where you meet alot of international people. It's located around different embassies, whom may provide a tour. Theres tons of people from over the world! Tons of different restaurants, and cultural things to do. Less than a mile walk to Dupont Circle, U Street, Adams Morgan, Columbia Heights metro. Tons of buses, and underground metro at your fingertips I can provide groceries for an affordable fee2015-05-12tt100%2015-03-01t2017-01-2238.925627-77.034596Great studio in midtown DCColumbia Heights440.0NaN200090.01.0
5124229354.442651ApartmentPrivate room{TV,"Wireless Internet",Heating,"Smoke detector","Carbon monoxide detector","First aid kit","Fire extinguisher",Essentials,Hangers,"Laptop friendly workspace"}21.0Real BedstrictTrueSFBeautiful private room overlooking scenic views in San Francisco's upscale Noe Valley neighborhood. You'll have your own bedroom, queen bed, dresser, wardrobe, smart TV, workstation, desk&chair, WiFi, and kitchen. Fresh towels and linens are provided for your convenience. MUNI bus stop and all tech shuttles are around the corner, J-Church subway is 5-minute walk away. Restaurants, bars, and cafes are around the corner. Ideal location for experiencing SF or commuting to Silicon Valley. We'll provide 100% cotton sheets and fresh towels. You'll have access to the full size bathroom as well as the kitchen. We'll also provide high speed business class WiFi internet, as well as HBO and Net"ix on your private TV. Guests will have access to kitchen and bathroom in addition to their private bedroom. We'll interact with you as much as you'd like to make your stay as comfortable as possible. We are available if you need us but will not disturb you otherwise. One of the sunniest neighborhoods in S2017-08-27tt100%2017-06-07t2017-09-0537.753164-122.429526Comfort Suite San FranciscoNoe Valley3100.0https://a0.muscache.com/im/pictures/82509143-4b21-44eb-a556-e3c1e0afac60.jpg?aki_policy=small941311.01.0
6118255294.418841ApartmentEntire home/apt{TV,Internet,"Wireless Internet","Air conditioning",Pool,Kitchen,"Free parking on premises",Gym,"Elevator in building","Hot tub",Heating,"Family/kid friendly",Washer,Dryer,"Smoke detector","Carbon monoxide detector",Essentials,Shampoo,Hangers,"Hair dryer","Laptop friendly workspace"}31.0Real BedmoderateTrueLAWarm and cozy studio with full kitchen and bathroom. Big balcony. Parking included. Walk to the beach, rent a bike, eat at variety of restaurants, see boats at Marina harbor! Great location! Friendly neighborhood! Meet locals at "Cow's End" famous coffee and wrap cafe. Great experience all around. This is a non smoking apartment. Warm and cozy studio with full kitchen and bathroom. Big balcony. Parking included. Walk to the beach, rent a bike, eat at variety of restaurants, see boats at Marina harbor! Great location! Friendly neighborhood! Meet locals at "Cow's End" famous coffee and wrap cafe. Great experience all around. Close to the beach, lots of entertainment, balcony, full kitchen. Gated parking, swimming pool, beach activities, Venice Beach, Marina Harbor At the check in, to deliver the keys. Quite peaceful place, friendly locals, fresh ocean breeze, Venice canals, marina harbor. Yes, the is Bus and uber Gated parking, swimming pool, beach activities, Venice Beach, Marina Har2017-03-10tf100%2017-03-03t2017-04-2133.980454-118.462821Beach Town Studio and Parking!!!11hNaN1597.0https://a0.muscache.com/im/pictures/4c920c60-43dc-4169-a0da-ccf37f1d7a94.jpg?aki_policy=small902921.01.0
7139712734.787492CondominiumEntire home/apt{TV,"Cable TV","Wireless Internet","Wheelchair accessible",Pool,Kitchen,"Free parking on premises",Doorman,Dog(s),Cat(s),"Other pet(s)","Elevator in building","Hot tub","Buzzer/wireless intercom",Heating,"Family/kid friendly",Washer,Dryer,"Smoke detector","Carbon monoxide detector","First aid kit",Essentials,Shampoo,"Lock on bedroom door",Hangers,"Hair dryer"}21.0Real BedmoderateTrueLAArguably the best location (and safest) in downtown LA. Steps away from Staples Center, LA Live, LA Convention Center, 2 metro stations. Next door to FIDM. Convenience: Starbucks & cleaner in the building. Ralph's across the street. FREE ASSIGNED GATED INDOOR PARKING. FREE WIFI. Fully stocked kitchen. PANORAMIC view of DTLA neighborhoods and LA Live, on your private balcony that spans the entire living room and bedroom. Luxury hotel-style living with concierge, lobby, pool, jacuzzi, etc! Bright, airy and cozy 1 bedroom condo - all to yourself! 1 assigned parking space (indoor, gated), at no additional charge. Condo is located in South Park, the sub-neighborhood of Downtown LA with premium luxury high rise buildings (Ritz-Carlton, EVO, and many more). It is next to the Financial District and a stone's throw away from Korea Town and Little Tokyo. Several blocks away from 4 major freeways: 110, 5, 10 and 101. Several blocks away from 4 major freeways: 110, 5, 10 and 101. 3 blocks aw2016-12-16tt100%2013-05-18f2017-04-1234.046737-118.260439Near LA Live, Staple's. Starbucks inside. OWN VIEWDowntown993.0https://a0.muscache.com/im/pictures/61bd05d5-c4db-4c49-9f87-c0981c2d83b9.jpg?aki_policy=small900151.01.0
81807924.787492HousePrivate room{TV,"Cable TV","Wireless Internet","Pets live on this property",Dog(s),Heating,"Smoke detector","First aid kit",Essentials,Shampoo,"Lock on bedroom door",Hangers,"Hair dryer","Private entrance","Hot water","Bed linens","Extra pillows and blankets","Coffee maker",Refrigerator,"Dishes and silverware","Garden or backyard"}21.0Real BedmoderateTrueSFGarden Studio with private entrance from the street and garden patio sitting area in a charming house in front of Lincoln Park(URL HIDDEN) Queen bed, en suite bathroom and Breakfast bar. Quiet neighborhood away from the crowds. Great location. Your private entrance leads to a beautiful garden-patio,an excellent place to enjoy a cup of coffee or a glass of wine. When you enter to your private room you will find a comfortable Queen size bed, en suite bathroom,closet, a table for two and a Breakfast Bar area with mini-fridge, coffee maker,kettle, toaster oven and few goodies awaiting for you. Please note, there is no kitchen. Flat screen CableTV, Free Wifi and maps of the area as well. This is a studio in a garden level of a private home. Our family living space is above it and there can be a modest amount of noise briefly in the morning and again in the evening. People enjoy having their own separate entrance, an added bonus to an already great and welcoming space. W2016-02-13tf100%2015-06-04f2017-09-2437.781128-122.501095Cozy Garden Studio - Private EntryRichmond District15999.0https://a0.muscache.com/im/pictures/0ed6c128-7d60-4e05-b3bf-63158a230f70.jpg?aki_policy=small941211.01.0
953852603.583519HousePrivate room{"Wireless Internet","Air conditioning",Kitchen,"Free parking on premises",Heating,"Family/kid friendly",Washer,Essentials,Shampoo,Hangers,"Hair dryer","Laptop friendly workspace","translation missing: en.hosting_amenity_50"}21.0Real BedmoderateTrueLAQuiet community. Close to supermarkets,restaurants,60 freeway.Looking forward to provide you with high quality service 安全宁静的街区, 近华人及韩国超市,餐厅,近60号高速。欢迎旅行商住 宽敞、明亮、干净、整洁、出入方便 Available kitchen,laundry,living room 可使用厨房洗衣间及客厅 Anytime contact with message or email.phone call available 9am to 7pm. 任何时间的信息和邮箱都可以。电话请在太平洋时间早上9点至晚上7点之间 房客须知: 1、房客之间不允许私下擅自更换房间,必须经由管理员,以及双方房客许可,由管理员监督更换房间。 1, between the tenants are not allowed to privately change the room, must be by the administrator, and the two sides permit tenants, supervise the replacement of the room by the administrator. 2、请认准自己预定房间的号码拿取钥匙,不得进入非预定房间。 2, please look for their own room number to pick up the key, not to enter the not you booking room. 3、预定房间请看准描述选定房间,如描述不清请向管理员咨询,预定后既视为认可不给予任何更换。 3, booking room, please see the description of the selected room, such as the description is unclear, please ask to the administrator, when confired booked room,we don't given any replacement. 4、请自行确认是否预定想要的房源,如不清楚请及时询问管理员,如确认预定,管理员既视为满意本房源,不再对预定进行2017-04-03tf100%2017-03-12t2017-04-1633.992563-117.895997No.7 Queen Size Cozy Room 舒适大床房NaN290.0https://a0.muscache.com/im/pictures/8d2f08ce-bf65-4018-a7b0-18823a7882a7.jpg?aki_policy=small917481.01.0
idlog_priceproperty_typeroom_typeamenitiesaccommodatesbathroomsbed_typecancellation_policycleaning_feecitydescriptionfirst_reviewhost_has_profile_pichost_identity_verifiedhost_response_ratehost_sinceinstant_bookablelast_reviewlatitudelongitudenameneighbourhoodnumber_of_reviewsreview_scores_ratingthumbnail_urlzipcodebedroomsbeds
74101180777174.584967ApartmentPrivate room{TV,Internet,"Wireless Internet",Pool,Kitchen,"Free parking on premises",Heating,Washer,Dryer,"Smoke detector",Shampoo,"Hair dryer"}11.0Real BedmoderateTrueLAA great comfortable bedroom available in the heart of Santa Monica. Two blocks from 3rd Street Promenade and just a few blocks from the beach. Walk to many wonderful restaurants and shops.2016-03-26tt100%2014-04-01f2017-04-1134.020021-118.498239Cozy room in heart of Santa Monica!NaN490.0https://a0.muscache.com/im/pictures/9dc32319-3ca5-4140-a7db-abff00e2ca87.jpg?aki_policy=small904031.01.0
7410279359344.110874VillaShared room{TV,"Wireless Internet","Air conditioning",Kitchen,"Free parking on premises",Breakfast,"Indoor fireplace",Heating,"Family/kid friendly",Washer,Dryer,"Smoke detector","Carbon monoxide detector","First aid kit","Safety card","Fire extinguisher",Essentials,Shampoo,Hangers,"Hair dryer",Iron,"Laptop friendly workspace"}103.0Real BedflexibleFalseLAThe price is for room. You will share the house with my family . 我的房源适合情侣、独自旅行的冒险家、商务旅行者、有小孩的家庭、大型团体。 Single-family house located at West Hills Residential area of L.A. There are 4 private rooms with 2 bathrooms for rent, in a very nice neighbourhood. You will stay with my family and my son(1 year old) will give you a passionate welcome. U will share the house with my (URL HIDDEN) house is in West Hills (91307). 2 people per ROOM only! keep quite and gentle!2017-01-01tf100%2013-07-01f2017-01-0134.199671-118.618070UR SWEET HOME IN LAWest Hills180.0https://a0.muscache.com/im/pictures/27fea634-a93e-450d-951a-41ef9f3abab8.jpg?aki_policy=small913071.010.0
74103118290115.135798ApartmentEntire home/apt{TV,"Cable TV",Internet,"Wireless Internet","Air conditioning","Wheelchair accessible",Gym,Elevator,"Indoor fireplace",Heating,"Smoke detector","Carbon monoxide detector","First aid kit","Safety card","Fire extinguisher",Essentials,Shampoo,"24-hour check-in",Hangers,"Hair dryer",Iron,"Laptop friendly workspace","Self Check-In",Doorman}21.0Real BedmoderateTrueSFCENTRALLY LOCATED Studio Unit in the WorldMark® by Wyndham San Francisco Resort. Super convenient downtown spot. Walk to Union Square and Moscone. 24/7 front desk for your late check-in convenience, early luggage storage, and general assistance. Exciting urban location: 2 blocks from Union Square, a short walk to Chinatown and Nob Hill. The famous Powell Street Cable Car stops just one block away, and public transportation on the Bay Area Rapid Transit system (Bart) is nearby. This WorldMark® by Wyndham resort is in RCI Silver Crown category. So you know it's a high quality resort. And with the consistent high standard of WorldMark resorts, you are not entering the Airbnb lottery here. You can be assured that you're staying at a professionally managed resort and enjoying a comfortable and welcoming stay. Try WorldMark once and you would want to come back! Some units are actually more spacious than an hotel room. Others are cozy. All are well equipped, and nicer than most hote2011-12-10tf100%2011-09-26f2015-11-1337.789989-122.407384WorldMark W'ndm Union Square StudioUnion Square2498.0https://a0.muscache.com/im/pictures/9a99e2de-c163-4fdb-aed3-b054f9324920.jpg?aki_policy=small941081.01.0
74104149341124.356709ApartmentEntire home/apt{TV,"Cable TV",Internet,"Wireless Internet","Air conditioning","Wheelchair accessible",Pool,Kitchen,Doorman,Gym,"Elevator in building",Heating,"Family/kid friendly",Washer,Dryer,"Smoke detector","Carbon monoxide detector","First aid kit","Safety card","Fire extinguisher",Essentials,Shampoo,Hangers,"Hair dryer",Iron,"Laptop friendly workspace"}21.0Real BedstrictTrueChicago*Location location location!!! 1B,1BA available at State and Rush: center of Gold Coast premium real estate, retail and restaurants! Fully furnished, huge closets, free wifi and direct TV, updated gym and laundry in building. *Friendly neighbors and 24hr security doormen. *Walking distance to red line stop (Chicago or Division ) Entire apt is yours. 1000 square feet. Private balcony w view. Utilities, Gym, laundry, free wifi and cable, Varies. I prefer to respect their privacy if requested. I am a socialite so I love meeting new people and making new friends! Walking distance to everything: *Oak street elite boutiques *Michigan Ave-Magnificent Mile *Barney's, Bloomingdales and Water Tower *Oak Street and North Ave beaches *Red line train is 2 blocks away *Starbucks *Gibson's Steakhouse *Tavern on Rush *Lou Malnatis famous deep dish pizza *The Original Pancake House *Dublin's Irish Pub *Division and State street bar crawl 2 blocks from the red line. Bus stop right infront I am prett2014-04-28tt100%2014-03-01f2017-04-0841.909067-87.623046Gold Coast SpecialtyNaN6100.0https://a0.muscache.com/im/pictures/32922903/b57ee870_original.jpg?aki_policy=small606101.01.0
741058088024.248495HousePrivate room{TV,Internet,"Wireless Internet","Air conditioning",Kitchen,"Free parking on premises","Pets live on this property",Heating,Washer,Dryer,"Smoke detector","24-hour check-in",Hangers}21.0Real BedmoderateTrueLAPerfect for 1 person, will accept couples at additional fee. Large closet with mirrored door. Your very own bathroom with shower! Tons of restaurant options, Free street parking or a gated parking space! A sweet shy dog, large shared kitchen, dining area, living room! Free wifi! WELCOME TO WeHo!!! This is a room in a small house behind a larger house, with gate entry for vehicle or just yourself! Close to Runyon Canyon, and much more! We have a company clean our house every few weeks, but we like to keep the place spotless in between visits! My dog usually hangs in my bed room while I'm at work. She's shy but very loving. House trained with a dog walker. She also does a few tricks! We share a living room, kitchen, and dining room with you! Washer and dryer, dishwasher, walking distance to tons of places! You have a private bathroom attached to your room. The room is furnished with a full bed and plenty of clean towels! Large closet with mirrors, dresser, and night stand. There is litt2016-03-25tt75%2015-03-18f2017-02-1934.092640-118.343921Private Bed/Bathroom in Cute House!NaN3691.0https://a0.muscache.com/im/pictures/ae2e48de-6045-49a7-a3bb-7dc1a9459a63.jpg?aki_policy=small900381.01.0
74106145492874.605170ApartmentPrivate room{}11.0Real BedflexibleFalseNYCone room in bushwick aptNaNttNaN2013-03-24fNaN40.709025-73.939405one room bushwickWilliamsburg0NaNhttps://a0.muscache.com/im/pictures/55162426/6016bc16_original.jpg?aki_policy=small11206.01.01.0
74107132818095.043425ApartmentEntire home/apt{TV,"Cable TV",Internet,"Wireless Internet",Kitchen,"Free parking on premises",Heating,"Family/kid friendly",Washer,Dryer,"Smoke detector",Essentials,Shampoo,Hangers,"Hair dryer","translation missing: en.hosting_amenity_50"}42.0Real BedmoderateTrueLALocated on the Pacific Coast Highway, this apartment is extremely spacious and convenient to downtown Hermosa Beach and Hermosa Beach Pier. There are 2 bedrooms with 2 double beds in each. Enjoy easy access to public transportation (bus stop right next door), or a dedicated parking space is available.2016-08-15tf100%2016-05-03f2017-04-1533.871549-118.396053Spacious Hermosa 2 BR on PCHHermosa Beach1693.0https://a0.muscache.com/im/pictures/2b86560b-a94e-427c-bafd-7971a3edca9b.jpg?aki_policy=small902542.04.0
74108186880395.220356ApartmentEntire home/apt{TV,Internet,"Wireless Internet","Air conditioning",Kitchen,Gym,Elevator,"Buzzer/wireless intercom",Heating,"Family/kid friendly",Washer,Dryer,"Smoke detector","Carbon monoxide detector","First aid kit",Essentials,Shampoo,Hangers,"Hair dryer",Iron,"Laptop friendly workspace","Hot water","Bed linens",Microwave,"Coffee maker",Refrigerator,Dishwasher,"Dishes and silverware","Cooking basics",Oven,Stove}51.0Real BedmoderateTrueNYCA modern apartment located in East Williamsburg. It's a place to see everything Brooklyn has to offer, from interesting restaurants, hidden breweries and the region's long-time residents. You can explore the neighborhood, including nearby Bushwick or hop on the subway and be in Manhattan in 10 minutes. The apartment has a rooftop overlooking the city, hardwood floors, a fully stocked kitchen and plenty of space for your group. It's a great modern apartment with all the amenities one could need for a home-away-from-home stay. Located in a part of Williamsburg that's surrounded by cool places but also with plenty of history. You'll have access to my entire apartment, of course, as long with the building facilities. This includes a gym with weights and running machines, a rooftop with furniture and city view, along a recreation room and laundry machines. There's also an off-street bike rack guests can use. I don't pay for parking, so you'll have to park on the street. It's unpermited and2015-01-03tt100%2012-01-05t2017-09-1040.706749-73.942377Modern 2 Bedroom Apartment in WilliamsburgWilliamsburg4394.0https://a0.muscache.com/im/pictures/7fbe448c-5293-4a22-a83e-54c8bc1bbf0d.jpg?aki_policy=small11206.02.02.0
74109170459485.273000ApartmentEntire home/apt{TV,"Wireless Internet","Air conditioning",Kitchen,Heating,Washer,Dryer,"Smoke detector","Carbon monoxide detector",Essentials,Shampoo,Hangers,"Hair dryer",Iron,"Laptop friendly workspace"}21.0Real BedstrictTrueNYCFully renovated, designer's studio located in one of the most desirable areas of Manhattan (West Village/Chelsea). Comfortable bed, sofa, 50' TV with Netflix & Amazon Prime, full kitchen with dishwasher and all the necessities for cooking. Apartment is on the 2nd floor (only 1 flight up). Access to everything in the apartment, but nothing should go out of the apartment:) I am always available upon request. I always try to make my guest's experience as pleasant as possible. If you need directions, advice, transportation etc all you need to do is ask.NaNtf100%2017-09-17tNaN40.738535-74.000157Designer's Apartment in HEART of NYCWest Village0NaNhttps://a0.muscache.com/im/pictures/b3971b63-06d9-4417-86ca-e6b40c22edca.jpg?aki_policy=small100110.02.0
7411035348454.852030BoatEntire home/apt{TV,Internet,"Wireless Internet",Kitchen,"Free parking on premises",Heating,"Family/kid friendly","Smoke detector","Carbon monoxide detector","First aid kit","Safety card","Fire extinguisher",Essentials,Shampoo,"24-hour check-in",Hangers,"Hair dryer","Laptop friendly workspace"}41.0Real BedmoderateFalseLAYou will stay-aboard the Island Trader at one of the largest ports in the world, You're right in the heart of downtown Long Beach California, walking distant to many events and shopping, including the Long Beach Convention Center, Conference Center, The Queen Mary Ship, The Pike, Pine Street the Pacific Aquarium and much more. (Island Trader) is a private commercial charter boat as well, so if you would like to take a private harbor, sunset or whale watching during your stay just let us know. The Island Trader has comfortable sleeping quarters for up to four people, there is a bathroom onboard, fresh water, a galley with a refrigerator, toaster and microwave oven . Private hot showers are available with just a short walk up on the docks. Towels and linen are provided. Directv and Free WIFI Start your day with a unique shopping experience at Shoreline Village with all of its specialty shops. Next, take a walk over to the Long Beach Aquarium of the Pacific and take a journey of discovery2013-09-05tt100%2012-11-26f2017-04-3033.761096-118.192409Cozy Boat at Shoreline VillageLong Beach20596.0https://a0.muscache.com/im/pictures/22968537/da0156bc_original.jpg?aki_policy=small908021.02.0